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Strategies for integration of 3-D experimental data with modeling and simulation

  • Large Datasets in Materials Science, Part I
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Abstract

For the most comprehensive modeling and prediction of materials behavior at the microscale, experimentally measured three-dimensional (3-D) microstructural datasets must be incorporated as initial input into computational models. Although the capability to collect and store large amounts of 3-D microstructural data is advancing continuously, computational resources for the processing and simulation can limit the amount of data that can be analyzed. Depending on the features and properties of interest, several approaches can be applied to optimize processing, reduce the amount of data that needs to be simulated, and increase the efficiency of simulations to maximize the statistical significance of microstructure analyses. This paper presents examples of four such approaches to efficient integration of large 3-D datasets into modeling and simulations of mechanical behavior in an efficient yet statistically significant manner.

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Authors and Affiliations

  1. Naval Research Laboratory, Washington, D.C., USA

    A. C. Lewis, S. M. Qidwai & A. B. Geltmacher

  2. BlueQuartz Software, 400 South Pioneer Blvd., Springboro, OH, 45066, USA

    M. Jackson

Authors
  1. A. C. Lewis
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  2. S. M. Qidwai
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  3. M. Jackson
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  4. A. B. Geltmacher
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Correspondence to A. C. Lewis.

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Lewis, A.C., Qidwai, S.M., Jackson, M. et al. Strategies for integration of 3-D experimental data with modeling and simulation. JOM 63, 35–39 (2011). https://doi.org/10.1007/s11837-011-0043-0

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  • DOI: https://doi.org/10.1007/s11837-011-0043-0

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